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Proceedings Paper

Influence of textures on the robustness of the depth estimation algorithm for light field
Author(s): Shuo Peng; Yao Hu; Qun Hao
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Paper Abstract

3D reconstruction of scenes has been widely used in many fields including computer vision, virtual reality, mapping and so on. The light field camera, which could be easily built by inserting a microlens array in the traditional camera, has a simple structure as well as the capability to catch 4D information in one shot and implement 3D reconstruction by follow-up data processing. For these features, light field 3D reconstruction has a promising prospect in scenes with confined space or the need for real time imaging. In the light field 3D reconstruction process, the accurate match between pixels and image planes is a key for exactly recovery of depth information. Simultaneously using defocus information and correspondence information yields good results in many scenes by combining the merits of both methods. Defocus information is based on the difference of special points while correspondence information is based on the angular invariance of one point. Hence, both of them are significantly influenced by textures of targets. This paper focuses on the influence of textures on the robustness of the depth estimation algorithm. First, we made a theoretical analysis of the relationship between these two kind of information and the texture of target based on the information extracting methods. Next, we simulated the information extracting and depth reconstruction based on the inverse of light field refocusing algorithm. After the analysis of the simulation, we gave some artificial textures for reference in the end. Our work in this paper could play a directive role in many applications such as adding specific texture to the surface of target to improve the accuracy of 3D reconstruction or the improvement of the algorithm for particular texture.

Paper Details

Date Published: 7 November 2018
PDF: 10 pages
Proc. SPIE 10817, Optoelectronic Imaging and Multimedia Technology V, 1081708 (7 November 2018); doi: 10.1117/12.2501095
Show Author Affiliations
Shuo Peng, Beijing Institute of Technology (China)
Yao Hu, Beijing Institute of Technology (China)
Qun Hao, Beijing Institute of Technology (China)
Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 10817:
Optoelectronic Imaging and Multimedia Technology V
Qionghai Dai; Tsutomu Shimura, Editor(s)

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